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材料信息学框架,以加速发现高反射率二维材料

Liudmila A Klimova¹, Ivan A Kruglov¹, Georgy A Ermolaev¹

¹Emerging Technologies Research Center, XPANCEO, Internet City, Emmay Tower, Dubai, United Arab Emirates.

|January 13, 2026

概括

我们开发了一个以物理为导向的机器学习模型,以预测二维 (2D) 材料的光学特性. 这加速了用于光子应用的新二维材料的发现.

科学领域:

材料科学材料科学材料科学
凝聚物质物理学凝聚物质物理学
计算材料科学科学计算材料科学

背景情况:

预测二维材料的光学性质对于光子学至关重要,但由于理论和实验之间的差异,这具有挑战性.
现有的方法难以准确和高效地选特定应用的新型二维材料.

研究的目的:

开发一个以物理为导向的机器学习 (ML) 框架,用于加快对二维材料的选.
为了能够准确地预测光学特性,用于设计下一代光子材料.

主要方法:

结合第一原则密度函数理论 (DFT) 计算与图形神经网络 (GNN) 模型.
综合实验光谱验证和考希模型用于光学属性校正.
开发了一种定义二维材料厚度的通用方法,并纠正了DFT衍生的光学特性.

主要成果:

创建了一个数据库,包含1000多种过渡金属二甲基化物 (TMD) 单层及其光学特性.
开发了一种ML模型,用于准确计算近红外 (近红外) 区域 (755-1064nm) 的折射率.
与独立的二维材料数据库验证了ML模型,证实了结构-属性相关性.

结论:

ML框架显著加快了2D材料的选,并提供了定制的光学功能.
像Bi2Te2Se这样的高折射率材料显示出在单层波导中增强场域封闭的希望.
这种方法有助于发现和设计用于集成光子学的新二维材料.

关键词:

两维材料是二维材料.密度函数理论密度函数理论图形神经网络的神经网络机器学习框架机器学习框架材料信息学材料信息学折射率指数的折射率指数是什么过渡金属二甲基二甲基化物

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